1. Field of the Invention
The present invention generally relates to a wireless sensor network and a sampling rate allocation method thereof, in particular, to a wireless sensor network which has long life time and meets the requirements to both total sample number and fairness, and to a sampling rate allocation method thereof.
2. Description of Related Art
A wireless sensor network is composed of a plurality of wireless sensors, and which can be used for environment monitoring by periodically sensing various samples (for example, temperature, humidity, sound, radio frequency, and atmospheric pressure) via these wireless sensors. The number of samples sensed by the wireless sensors in a specific time interval is referred as sampling rate. In addition, besides being applied to environment monitoring, a wireless sensor network may also be applied to various aspects in our daily life or industrial processes, such as speech recognition, object localization, and system modeling etc.
FIG. 1 is a schematic diagram of a wireless sensor network 10. Referring to FIG. 1, the wireless sensor network 10 includes a plurality of nodes 100, 101, . . . , 107 and a host 108, wherein the nodes 100, 101, . . . , 107 and the host 108 are connected to each other and communicate through radio wave or infrared light so as to send sensed samples and parameters for allocating the sampling rate. The nodes 100, 101, . . . , 107 and the host 108 may have sensors for obtaining samples; however, some of the nodes 100, 101, . . . , 107 or the host 108 may have no sensor but simply receive and send samples (P.S. the host 108 does not send samples). The nodes 100, 101, . . . , 107 of the wireless sensor network 10 may be wireless communication units with sensors, and the host 108 may be referred as a root node. The host 108 receives and analyzes samples sensed by each of the nodes 100, 101, . . . , 107.
The quality of samples sensed by the wireless sensor network 10 is related to the total number of samples and the source of the samples. Generally speaking, to ensure the reliability of the samples sensed by the wireless sensor network 10, the total number of samples in a specific time interval is increased and different sampling rates are allocated regarding to the significances of different nodes, so as to meet the requirements to both total sample number and fairness. However, an over-large total sample number won't increase the quality of the sensed samples drastically. To truthfully reconstruct the monitored phenomenon, the sampling rate of the wireless sensor network must be at least twice the highest frequency of the original signal, according to the Nyquist-Shannon sampling theorem (Refer to A. J. Jerri, “The Shannon Sampling Sheorem—Its Various Extensions and Applications: A Tutorial Review.”, Proceedings of the IEEE, 65(11):1565-1596, 1977.). Taking a sound capturing system as an example, the quality of the samples is not increased obviously when the total number of samples is about 3000 samples per second.
The life time of the wireless sensor network 10 refers to the time when the energy of a particular node (one of the nodes 101˜107) is used up, and the monitored phenomenon cannot be reconstructed. Generally speaking, the sampling rate of the node is related to the life time of the wireless sensor network. This is because that the energy of a node is consumed whenever the node senses, sends, or receives a sample. Accordingly, the more samples the node senses, sends, or receives, the more energy of this node will be consumed, and which will shorten the life time of the entire wireless sensor network 10. Thus, the higher sampling rate a node has, the shorter the life time of the node is. The sampling rate of a node is increased along with the total number of samples. As a result, over sampling cannot increase the quality and reliability of the samples; instead, additional energy is consumed and the life time of the wireless sensor network 10 is even shortened.
Several sampling rate allocation methods for wireless sensor networks have been disclosed in U.S. Patent No. 20060206857, U.S. Patent No. 20070058664, U.S. Pat. No. 7,002,501, European Patent. No. 20050250440, and some other articles. According to these methods, a host is allowed to receive the most samples before the energy of a node is used up first. However, in these methods, the samples may all come from some particular nodes and induce bias in data analysis. According to these methods, nodes having more energy are used for sensing more samples so that the life time of the wireless sensor network can be prolonged. However, in the worst case, all the samples may come from the same node, and since the samples obtained from the same node cannot represent the characteristic of the entire network, analysis error may be caused. A host should be able to receive samples from different nodes so as to avoid bias in data analysis.
Articles for providing foregoing methods include: [1] J. H. Chang and L. Tassiulas, “Maximum lifetime routing in wireless sensor networks,” IEEE Trans. on Networking., vol. 12, no. 4, pp. 609-619, August 2004. [2] J. Park and S. Sahni, “An Online Heuristic for Maximum Lifetime Routing in Wireless Sensor Netzvorks,” IEEE/ACM Transactions on Networking, 12(4):609-619, 2004. [3] K. Kar, M. Kodialam, T. V. Lakshman, and L. Tassiulas., “Routing for Network Capacity Maximization in Energy-constrained Ad-hoc Networks,” In INFOCOM '03: Twenty-Second Annual Joint Conference of the IEEE computer and Communications Societies, pages 673-681. IEEE Press, 2003. [4] Madan, R. Lall, S., “Distributed Algorithms for Maximum Lifetime Routing in Wireless Sensor Networks,” IEEE Transaction on Wireless Communications, 5(8):2185-2193, 2006. [5] A. Sankar and Z. Liu., “Maximum Lifetime Routing in Wireless Ad-Hoc Networks,” In INFOCOM '04: Twenty-Third Annual Joint Conference of the IEEE computer and Communications Societies, pages 1089-1097. IEEE Press, 2004.
In addition, another type of sampling rate allocation methods is disclosed in some other articles. According to these methods, a utility function is allocated to each node, wherein the value of the utility function increases along with the sampling rate of the node, and the increment thereof is reduced along with the increasing of the sampling rate. In these methods, the sampling rate of each node is allocated such that the total value of the utility functions of all these nodes can be maximized before a node runs out of its energy. Through these methods, the problem of lack of fairness in foregoing patents and articles can be resolved; however, a host may not be able to receive enough number of samples, namely, the requirement of total sample number cannot be met.
Articles for providing foregoing methods include: [1] Y. Cui, Y. Xue, and K. Nahrstedt, “A Utility-based Distributed Maximum Lifetime Routing Algorithm for Wireless Networks,” in IEEE Transactions on Vehicular Technology, 55(3):797-805, 2006. [2] Y. T. Hou, Y. Shi, and H. D. Sherali, “Rate allocation in wireless sensor networks with network lifetime requirement,” in Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), pp. 67-77, May 24-26, 2004, Tokyo, Japan. [3] V. Srinivasan, C. F. Chiasserini, P. Nuggehalli, and R. R. Rao., “Optimal Rate Allocation for Energy Efficient Multipath Routing in Ad Hoc Wireless Net-works,” IEEE Transactions on Wireless Communications, Vol. 3, May 2004. [4] J. H. Zhu, Shan Chen, B. Bensaou, and K. L. Hung, “Tradeoff between Lifetime and Rate Allocation in Wireless Sensor Networks: A Cross Layer Approach,” in IEEE INFOCOM 2007, May 2007.
In short, even though the sampling rate allocation methods disclosed in foregoing patents and articles can prolong the life time of the entire wireless sensor network, they cannot meet the quantity and fairness requirements. In addition, the collected samples may not be able to reconstruct the monitored phenomenon.